Multi-sensor visual analytics includes visualization of large scale multi-dimensional sensor data. Complex machines such as aircraft, vehicles and engines are fitted with numerous sensors that produce large volumes of data for each second of operation. Said large volumes of data generated by the sensors are to be analyzed by experts, for instance engineers. In certain instances, a machine designed for a particular application may also be used in other scenarios or applications. For instance, an engine may be used in a crane, pump or wood chipper.
It may be prudent to understand the behavior of machines in different applications, to test design hypotheses against actual practice and support future design decisions. As a result, a large number of basic visualizations such as histograms and long multivariate time-series arising from data derived from numerous sensors embodied in multiple machines across many years may be generated, and analyzed. However, analysis of said data by performing visual analytic tasks on such large volumes of data may be challenging.
Traditionally, the data derived from the sensors is analyzed by using various techniques including dimensionality reduction, scale reduction, or data reduction methods first and then using data visualization techniques on such data-summaries.